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Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols

High-throughput, in vitro approaches for the evolution of enzymes rely on a random micro-encapsulation to link phenotypes to genotypes, followed by screening or selection steps. In order to optimise these approaches, or compare one to another, one needs a measure of their performance at extracting t...

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Autores principales: Dramé-Maigné, Adèle, Zadorin, Anton S., Golovkova, Iaroslava, Rondelez, Yannick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175308/
https://www.ncbi.nlm.nih.gov/pubmed/32069848
http://dx.doi.org/10.3390/life10020017
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author Dramé-Maigné, Adèle
Zadorin, Anton S.
Golovkova, Iaroslava
Rondelez, Yannick
author_facet Dramé-Maigné, Adèle
Zadorin, Anton S.
Golovkova, Iaroslava
Rondelez, Yannick
author_sort Dramé-Maigné, Adèle
collection PubMed
description High-throughput, in vitro approaches for the evolution of enzymes rely on a random micro-encapsulation to link phenotypes to genotypes, followed by screening or selection steps. In order to optimise these approaches, or compare one to another, one needs a measure of their performance at extracting the best variants of a library. Here, we introduce a new metric, the Selection Quality Index (SQI), which can be computed from a simple mock experiment, performed with a known initial fraction of active variants. In contrast to previous approaches, our index integrates the effect of random co-encapsulation, and comes with a straightforward experimental interpretation. We further show how this new metric can be used to extract general protocol efficiency trends or reveal hidden selection mechanisms such as a counterintuitive form of beneficial poisoning in the compartmentalized self-replication protocol.
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spelling pubmed-71753082020-04-28 Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols Dramé-Maigné, Adèle Zadorin, Anton S. Golovkova, Iaroslava Rondelez, Yannick Life (Basel) Article High-throughput, in vitro approaches for the evolution of enzymes rely on a random micro-encapsulation to link phenotypes to genotypes, followed by screening or selection steps. In order to optimise these approaches, or compare one to another, one needs a measure of their performance at extracting the best variants of a library. Here, we introduce a new metric, the Selection Quality Index (SQI), which can be computed from a simple mock experiment, performed with a known initial fraction of active variants. In contrast to previous approaches, our index integrates the effect of random co-encapsulation, and comes with a straightforward experimental interpretation. We further show how this new metric can be used to extract general protocol efficiency trends or reveal hidden selection mechanisms such as a counterintuitive form of beneficial poisoning in the compartmentalized self-replication protocol. MDPI 2020-02-13 /pmc/articles/PMC7175308/ /pubmed/32069848 http://dx.doi.org/10.3390/life10020017 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dramé-Maigné, Adèle
Zadorin, Anton S.
Golovkova, Iaroslava
Rondelez, Yannick
Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols
title Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols
title_full Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols
title_fullStr Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols
title_full_unstemmed Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols
title_short Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols
title_sort quantifying the performance of micro-compartmentalized directed evolution protocols
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175308/
https://www.ncbi.nlm.nih.gov/pubmed/32069848
http://dx.doi.org/10.3390/life10020017
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